CN109657070B - Construction method of terminal-assisted SWOT index system - Google Patents
Construction method of terminal-assisted SWOT index system Download PDFInfo
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Abstract
The invention relates to the field of terminal centralized storage, in particular to a terminal auxiliary deviceSWOTThe construction method of the index system comprises the following steps: step (a)S100, extracting keywords of a text data set based on a terminal; step (a)S200 keyword clustering andSWOTmapping an index system; step (a)S300, generating an index system weight suggestion. The automatic extraction and clustering of the keywords effectively saves the human resources of the expert and avoids to a certain extentSWOTInfluence of human interference factors in the system construction process.
Description
Technical Field
The invention relates to the field of terminal centralized storage, in particular to a construction method of a terminal auxiliary SWOT index system.
Background
The SWOT analysis method (wherein Strenghs: internal dominance factor, weakness: internal Weakness factor, opportunities: external threat factor, threads: external threat factor) is a classical competitive information analysis tool, proposed by the university of Harvard, K.J. Andruse in 1971 in its company strategic concept. The method mainly comprises the steps of widely surveying and information collecting around an analysis target, and then analyzing the collected information to judge external opportunities and external threats affecting the target, and internal advantages and disadvantages of target implementation. The SWOT analysis method can perform simple preliminary analysis, qualitatively understand the overall profile of the analysis target, and can also realize strategic strategy formation, implementation or control decision of the target.
Because the SWOT analysis method starts from the analysis target overall, the advantages, disadvantages, opportunities and threat factors influencing the target implementation can be clearly listed, and comprehensive analysis is performed, so that the complex factors influencing the target implementation are clarified, and a decision maker can clearly grasp the risks and opportunities possibly existing in the target implementation, thereby improving the decision accuracy. Therefore, the SWOT analysis method is now the most commonly used analysis tool in management and decision making by modern government departments and enterprises, and is widely applied and researched.
Based on the technical problems, a new construction method of a terminal-assisted SWOT index system needs to be designed.
Disclosure of Invention
The invention aims to provide a construction method of a terminal-assisted SWOT index system.
In order to solve the technical problems, the invention provides a construction method of a terminal-assisted SWOT index system, which comprises the following steps:
step S100, extracting keywords of a text data set;
step S200, keyword clustering and SWOT index system mapping; and
and step S300, generating an index system weight suggestion.
Further, the method for extracting the keywords of the text data set in the step S100 includes:
step S101, filtering stop words, namely after Chinese word segmentation is carried out on an acquired text data set, filtering the stop words in the text data by accumulating stop word lists formed by selection;
step S102, filtering specific words, searching the words through a search engine, judging the words with search results less than a threshold value as specific words, and filtering the specific words;
step S103, extracting keywords, wherein the keywords are extracted through an improved TF/IDF algorithm.
Further, the improved TF/IDF algorithm is:
1 (1)
2W i ＝{W|TF/IDF(w i )＞η}；
3W = u W i ；
4. The method is to
Wherein TF/IDF (w i ) TF/IDF weight for word w in text data labeled i; TF (w) i ) Frequency number of occurrence of word w in text data with index i; n is the number of text data contained in the text data set; d is the number of text data containing word w;
the method for extracting the keywords through the improved TF/IDF algorithm comprises the following steps:
calculating TF/IDF weight of keywords contained in each text data in the text data set through the method 1;
sorting according to the size of the TF/IDF weight of the keywords in each text data;
extracting keywords with weight values larger than a threshold value eta to form a keyword set Wi of text data with a label of i, wherein the Wi set of all the text data is summarized into a keyword W set of the text data set;
aiming at pairwise pairing of keywords in the W set, calculating a ratio C;
TF in 4 sum (W a ) Refers to the cumulative sum of the frequency of occurrence of a certain keyword a in a W set, TF sum (W b ) Refers to the cumulative sum of the frequency of occurrence of a certain keyword b in a W set, G (W) a ) The search page result number obtained by the keyword a in the search engine is referred to; g (W) b ) The result number of the search page obtained by the keyword b in the search engine is referred to; the ratio C is TF of a pair of keywords a and b sum The ratio of the product of the values and G values, and the keywords in the W set are ordered by the result of the ratio and displayed in order to correct the keywords.
Further, the method for mapping the keyword clusters and the SWOT index system in step S200 includes:
step S201, according to the Chinese classification subject word list, the primary classification of the keywords is realized, the keywords extracted from the current text data set are classified by contrasting with the Chinese classification subject word list, and an initial keyword classification structure is established;
step S202, clustering the residual keywords by adopting a K_MEANS clustering method according to the word proximity degree serving as a word-word distance measure for the keywords which cannot be classified correspondingly in the Chinese classification subject word list after preliminary classification;
step S203, after the terminal auxiliary clustering is completed, the clustered keywords are displayed and corrected in a classified manner;
step S204, after repeated iteration of keyword clustering and classification correction of clustered keywords, mapping the word classes into corresponding indexes according to classification information of the clustered word classes, namely
And establishing an index system of SWOT analysis.
Further, the method for generating the index system weight suggestion in the step S300 includes: selecting factors influencing weight judgment of an index system;
the factors influencing the weight judgment of the index system comprise:
word quantity of keywords contained in word class: judging the index weight generated by mapping each word class by analyzing the number of keywords contained in each word class generated in the keyword clustering process, namely, the index weight corresponding to the word class with the larger number of keywords is larger;
word frequency of keywords contained in word class: accumulating and summing the frequencies of all keywords contained in the word class in the text data set; and
the timeliness of keywords contained in the word class: word frequency statistics in the time dimension for keywords contained in a word class show how much attention is paid to the keywords in the time dimension.
Further, the method for generating the index system weight suggestion in the step S300 further includes: constructing a generation formula of index system weight suggestion based on factors influencing index system weight judgment, namely
Wherein R (W) is an index weight suggestion corresponding to a word class; i is the number of keywords contained in the word class from 1 to k, and the word class is sequentially divided into two parts of speechCalculating all keywords in the database; j is text data comprising a word w in the word class from 1 to d, and all text data comprising the word w are sequentially calculated; traversing text data containing word w, and respectively calculating the j-th time decay function containing word w; TF (w) j ) Is the frequency with which word w appears in text data j; e, e -μ(t-tc) As a function of time decay; mu is the decay constant; t is the time when the text data appears; tc is the current time;
an index weight suggestion is generated after calculating an R (W) weight suggestion value for each word class.
The method has the advantages that the method is based on keyword extraction of the text data set by the terminal, and keyword clustering and SWOT index system mapping are carried out; and generating an index system weight suggestion, so that automatic extraction and clustering of keywords are realized.
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The invention will be further described with reference to the drawings and examples.
Fig. 1 is a flowchart of a method for constructing a terminal-assisted SWOT index system according to the present invention.
Detailed Description
The invention will now be described in further detail with reference to the accompanying drawings. The drawings are simplified schematic representations which merely illustrate the basic structure of the invention and therefore show only the structures which are relevant to the invention.
Example 1
Fig. 1 is a flowchart of a method for constructing a terminal-assisted SWOT index system according to the present invention.
As shown in fig. 1, this embodiment provides a method for constructing a terminal-assisted SWOT index system, including:
step S100, extracting keywords of a text data set based on a terminal;
step S200, keyword clustering and SWOT index system mapping; and
step S300, generating an index system weight suggestion;
in this implementation, the terminal may, but is not limited to, use a computer to assist in the construction of the SWOT index system; the automatic extraction and clustering of the keywords effectively saves expert human resources, and avoids the influence of human interference factors in the SWOT system construction process to a certain extent.
In this embodiment, the step S100, the method for extracting the keywords from the text data set based on the terminal includes: step S101, filtering stop words, namely after Chinese word segmentation is carried out on a text data set acquired by a terminal, filtering the stop words in the text data through a stop word list formed by accumulation and selection, wherein the stop words are usually language aid words, virtual words, quantity words and the like; step S102, filtering specific words, searching the words through a search engine, judging the words with search results less than a threshold value as specific words, and filtering the specific words, wherein the specific words are generally specific words with strong directivity such as place names and person names; unlike stop words, specific words are difficult to filter through a custom word list, and in related research work, classification of the specific words is realized by using word classification reasoning, whether the words are place names, person names and the like is judged, but certain unreliability exists in the inference; judging the specific word by using a search engine such as Google, hundred degrees and the like; for example, google displays the number of search results each time, and searches using specific words can result in fewer search pages, so that words with search results less than a certain threshold value can be judged as specific words and filtered; the specific word searching of Google can be automatically completed through the algorithm of Google; step S103, extracting keywords, namely extracting the keywords through an improved TF/IDF algorithm; the TF/IDF algorithm is the key word extraction algorithm currently mainstream, and TF (Term Frequency) refers to the number of times a word appears in a certain text, and IDF (Inverse Document Frequency: inverse document Frequency).
In this embodiment, it is required to extract keywords in the set for the whole text data set, and the conventional TF/IDF algorithm is to extract keywords in a document for a certain document, so that the conventional TF/IDF algorithm is improved; the improved TF/IDF algorithm is:
1 (1)
2W i ＝{W|TF/IDF(w i )＞η}；
3W = u W i ；
4. The method is to
Wherein TF/IDF (w i ) TF/IDF weight for word w in text data labeled i; TF (w) i ) Frequency number of occurrence of word w in text data with index i; n is the number of text data contained in the text data set; d is the number of text data containing word w;
the method for extracting the keywords through the improved TF/IDF algorithm comprises the following steps: calculating TF/IDF weight of keywords contained in each text data in the text data set through the method 1; sorting according to the size of the TF/IDF weight of the keywords in each text data; extracting keywords with weight values larger than a threshold value eta to form a keyword set Wi of text data with a label of i, wherein the Wi set of all the text data is summarized into a keyword W set of the text data set; aiming at pairwise pairing of keywords in the W set, calculating a ratio C; TF in 4 sum (W a ) Refers to the cumulative sum of the frequency of occurrence of a certain keyword a in a W set, TF sum (W b ) Refers to the cumulative sum of the frequency of occurrence of a certain keyword b in a W set, G (W) a ) The search page result number obtained by the keyword a in the search engine is referred to; g (W) b ) The result number of the search page obtained by the keyword b in the search engine is referred to; the ratio C is TF of a pair of keywords a and b sum Value and G value (G value and TF) sum Refers to the ratio of the product of the search page results obtained in the search engine for a pair of keywords), and the keywords in the W set are ordered according to the result of the ratio and displayed in order to correct the keywords.
In this embodiment, the method for mapping the keyword clusters and the SWOT index system in step S200 includes: step S201, according to the Chinese classification subject word list, the primary classification of the keywords is realized, the keywords extracted from the current text data set are classified by contrasting with the Chinese classification subject word list, and an initial keyword classification structure is established; step S202, clustering the residual keywords by adopting a K MEANS clustering method according to the word proximity degree serving as a word-word distance measure for the keywords which cannot be classified correspondingly in the Chinese classification subject word list after preliminary classification; step S203, after the terminal assisted clustering is completed, the clustered keywords are displayed in a classified manner and corrected, wherein the correction method can be used for correcting by manual work; step S204, after repeated iteration of keyword clustering and classification correction of the clustered keywords, mapping the word parts into corresponding indexes according to classification information of the clustered word parts, namely establishing an index system of SWOT analysis.
In this embodiment, in step S300, the method for generating the index system weight suggestion includes: selecting factors influencing weight judgment of an index system; the support degree of each index in the index system for the analysis result is different, namely, some indexes are main factors, some indexes are secondary factors, and the weight suggestion is generated by three factors influencing the weight judgment of the index system in the embodiment; the factors influencing the weight judgment of the index system comprise:
word quantity of keywords contained in word class: judging the index weight mapped by each word class by analyzing the number of keywords contained in each word class generated in the keyword clustering process, namely, the index weight corresponding to the word class with the larger number of keywords is larger;
word frequency of keywords contained in word class: in addition to the number of keywords, the keyword word frequency contained in the word class is also the weight judgment basis of the index mapped by the word class, and the keyword word frequency contained in the word class is the frequency accumulated sum of all keywords contained in the word class in the text data set;
the timeliness of keywords contained in the word class: the frequency of the occurrence of the keywords in a certain time period is that a text data set acquired through open source data has time attributes, the time attributes of the text data are added to the words in the text data, the time attributes of the words are not examined when the keywords are analyzed and extracted, and word frequency statistics of the keywords contained in one word class in the time dimension shows the degree that the keywords are concerned in the time dimension, namely the timeliness of the keywords contained in the word class is also an element for judging the corresponding index weight of the keywords.
In this embodiment, in step S300, the method for generating the index system weight suggestion further includes: constructing a generation formula of index system weight suggestion based on factors influencing index system weight judgment, namely
Wherein R (W) is an index weight suggestion corresponding to a word class; i is the number of keywords contained in the word class from 1 to k, and all keywords in the word class are calculated in sequence; j is text data comprising a word w in the word class from 1 to d, and all text data comprising the word w are sequentially calculated; traversing text data containing word w, and respectively calculating the j-th time decay function containing word w; TF (w) j ) Is the frequency with which word w appears in text data j; e, e -μ(t-tc) As a function of time decay; mu is the decay constant; t is the time when the text data appears; tc is the current time; an index weight suggestion is generated after calculating an R (W) weight suggestion value for each word class.
In summary, the method and the device realize automatic extraction and clustering of keywords by extracting the keywords of the text data set, mapping the keywords by using the clustering of the keywords and the SWOT index system, and finally generating the index system weight suggestion, thereby effectively saving expert human resources and avoiding the influence of human interference factors in the construction process of the SWOT system to a certain extent; the method can also effectively save the workload of information analysts, and can reduce interference factors in the construction process of the SWOT index system to a certain extent, thereby having promotion significance for the application of SWOT analysis.
The method comprises the steps of obtaining keywords related to SWOT analysis targets through a first iteration, clustering the keywords, mapping the keyword class into SWOT evaluation indexes through targets of a second iteration, and generating suggestions of SWOT index weights through an algorithm after the SWOT evaluation indexes are generated in the last part.
With the above-described preferred embodiments according to the present invention as an illustration, the above-described descriptions can be used by persons skilled in the relevant art to make various changes and modifications without departing from the scope of the technical idea of the present invention. The technical scope of the present invention is not limited to the description, but must be determined according to the scope of claims.
Claims (3)
1. The construction method of the terminal-assisted SWOT index system is characterized by comprising the following steps of:
step S100, extracting keywords of a text data set;
step S200, keyword clustering and SWOT index system mapping; and
step S300, generating an index system weight suggestion;
the method for extracting the keywords of the text data set in the step S100 comprises the following steps:
step S101, filtering stop words, namely after Chinese word segmentation is carried out on an acquired text data set, filtering the stop words in the text data by accumulating stop word lists formed by selection;
step S102, filtering specific words, searching the words through a search engine, judging the words with search results less than a threshold value as specific words, and filtering the specific words;
step S103, extracting keywords, namely extracting the keywords through an improved TF/IDF algorithm;
the improved TF/IDF algorithm is:
1 (1)
2W n ＝{W|TF/IDF(w n )＞η}；
3W = u W n ；
4. The method is to
Wherein TF/IDF (w n ) TF/IDF weight for word w in text data numbered n; TF (w) n ) Frequency of occurrence of the word w in the text data with the number n; n is the number of text data contained in the text data set; d is the number of text data containing word w;
the method for extracting the keywords through the improved TF/IDF algorithm comprises the following steps:
calculating TF/IDF weight of keywords contained in each text data in the text data set through the method 1;
sorting according to the size of the TF/IDF weight of the keywords in each text data;
extracting keywords with weights larger than a threshold value eta to form a keyword set W of text data with a label of n n W of all text data n The collection summary is a keyword W collection of the text data collection;
aiming at pairwise pairing of keywords in the W set, calculating a ratio C;
TF in 4 sum (W a ) Refers to the cumulative sum of the frequency of occurrence of a certain keyword a in a W set, TF sum (W b ) Refers to the cumulative sum of the frequency of occurrence of a certain keyword b in a W set, G (W) a ) The search page result number obtained by the keyword a in the search engine is referred to; g (W) b ) The result number of the search page obtained by the keyword b in the search engine is referred to; the ratio C is TF of a pair of keywords a and b sum The ratio of the product of the value and the G value, and the keywords in the W set are ordered according to the result of the ratio and displayed in sequence to correct the keywords;
the step S200, the method for mapping the keyword clusters and the SWOT index system comprises the following steps:
step S201, according to the Chinese classification subject word list, the primary classification of the keywords is realized, the keywords extracted from the current text data set are classified by contrasting with the Chinese classification subject word list, and an initial keyword classification structure is established;
step S202, clustering the residual keywords by adopting a K_MEANS clustering method according to the word proximity degree serving as a word-word distance measure for the keywords which cannot be classified correspondingly in the Chinese classification subject word list after preliminary classification;
step S203, after the terminal auxiliary clustering is completed, the clustered keywords are displayed and corrected in a classified manner;
step S204, after repeated iteration of keyword clustering and classification correction of clustered keywords, mapping the word classes into corresponding indexes according to classification information of the clustered word classes, namely
And establishing an index system of SWOT analysis.
2. The construction method according to claim 1, wherein,
the method for generating the index system weight suggestion in the step S300 includes: selecting factors influencing weight judgment of an index system;
the factors influencing the weight judgment of the index system comprise:
word quantity of keywords contained in word class: judging the index weight generated by mapping each word class by analyzing the number of keywords contained in each word class generated in the keyword clustering process, namely, the index weight corresponding to the word class with the larger number of keywords is larger;
word frequency of keywords contained in word class: accumulating and summing the frequencies of all keywords contained in the word class in the text data set;
the timeliness of keywords contained in the word class: word frequency statistics in the time dimension for keywords contained in a word class show how much attention is paid to the keywords in the time dimension.
3. The construction method according to claim 2, wherein,
the method for generating the index system weight suggestion in the step S300 further includes: constructing a generation formula of index system weight suggestion based on factors influencing index system weight judgment, namely
Wherein R (W) is an index weight suggestion corresponding to a word class; i is the number of keywords contained in the word class from 1 to k, and all keywords in the word class are calculated in sequence; j is text data comprising a word w in the word class from 1 to d, and all text data comprising the word w are sequentially calculated; traversing text data containing word w, and respectively calculating the j-th time decay function containing word w; TF (w) j ) Is the frequency with which word w appears in text data j; e, e -μ(t-tc) As a function of time decay; mu is the decay constant; t is the time when the text data appears; tc is the current time;
an index weight suggestion is generated after calculating an R (W) weight suggestion value for each word class.
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